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DEVELOPMENT OF THE ODOO SYSTEM FOR THE EMPLOYEE PERFORMANCE APPRAISAL SYSTEM IN HRM MODULE
Get PDFIn organizations, such as companies, performance evaluation aims to assess, motivate, and improve employee performance. Human resource development is achieved by recognizing employees' potential. At PT.HM, there is currently no system for employee performance evaluation. The process is done manually by distributing Excel files for self-assessment, which are then forwarded for further evaluation. Performance evaluations are critical for measuring individual performance, serving as a basis for rewards and career paths. This research aims to develop an appraisal system in the Odoo HRM module using the NineBox Matrix, as PT.HM requires employee mapping across nine categories. The research includes four stages: problem identification, data collection, data analysis, and system design. The performance appraisal system will be integrated into the Odoo system already in use at the company for HRM. Data collection was done through direct observation at PT.HM. The system's development benefits HR Managers, Personal Managers, and employees by automating performance and competency assessments. It calculates scores based on predefined weights configured by HR Managers and provides a career path for employee promotions. The system is tested through black-box testing, yielding a 100% success rate, and User Acceptance Testing (UAT), also achieving a 100% success rate, as expected
ENHANCING HERBAL PLANT LEAF IMAGE DETECTION ACCURACY THROUGH MOBILENET ARCHITECTURE OPTIMIZATION IN CNN
Get PDFHerbal plants have various health benefits, but their type identification remains challenging for the general public. This study aims to improve the accuracy of herbal plant leaf classification using Convolutional Neural Network (CNN) based on MobileNetV2 architecture. To enhance model performance, various optimization techniques including fine-tuning, batch normalization, dropout, and learning rate scheduling were implemented. The experimental results showed that the proposed optimized model achieved an accuracy of 100%, significantly outperforming previous studies that used standard MobileNet with an accuracy of 86.7%. While these perfect results warrant additional validation with more diverse datasets to confirm generalizability, this study contributes to the development of a more accurate herbal plant classification system that is readily accessible to the general public. Future work should explore model performance under varying environmental conditions and with expanded plant species datasets
DIGITAL MARKETING MELALUI PEMBUATAN KONTEN PADA MEDIA SOSIAL SEBAGAI PENINGKATAN PROMOSI DAN BRANDING SEKOLAH
Get PDFDigital marketing has become one of the forms of marketing by utilizing digital platforms and technologies to connect with the target audience online. The use of digital marketing can be applied through digital-based media. Social media is integrated with various lines and used as a means of disseminating relevant content according to the target audience in achieving the organization's vision, mission, and goals. Based on the observation results, many teachers still have limited skills in creating digital content and marketing content. Therefore, the purpose of this community service activity is to enhance the marketing skills of teachers and educational staff through e-marketing-based activities for school social media. This community service activity was conducted in the Auditorium of Campus 3, Universitas Negeri Malang, Kepanjen Kidul, Kota Blitar. The partners in this community service activity are the PAUD teachers of Group 3, Kepanjen Kidul, Blitar City. The implementation method consists of three stages: needs analysis, execution, and evaluation. The result of the implementation of this program is content published in each institution as a means of promotion and branding. In addition, the PAUD teachers of Cluster 3 Kepanjen Kidul, Blitar City, gained knowledge and understanding both theoretically and practically, including understanding branding, design skills, website utilization, institutional branding enhancement, and skill sustainability. This understanding is expected to be continuously applied as an effort to optimize digital skills in the context of school branding at the early childhood education level
MULTIPLAYER ONLINE ROLE-PLAYING GAME VIRTUAL CLASSROOMS USING THE GAME DEVELOPMENT LIFE CYCLE METHOD
Get PDFThe COVID-19 pandemic has disrupted traditional education, forcing a shift toward online learning, which often lacks engagement and effectiveness. Existing virtual classroom methods struggle to sustain students' attention and motivation, leading to reduced learning outcomes. To address these issues, this study develops an innovative Virtual Classroom application based on Multiplayer Online Role-Playing Game (MORPG) technology. The goal is to provide a more interactive and immersive learning environment, enhancing engagement among students and lecturers. Using the Unity Game Engine, Photon Unity Networking (PUN), and Photon Voice libraries, this application transforms online classes into game-like experiences. The development followed the Game Development Life Cycle (GDLC) methodology, ensuring a structured and effective approach. Blackbox testing confirmed that all functions operated as intended, while usability testing with the System Usability Scale (SUS) among 30 users yielded an average score of 71.92, indicating a satisfactory experience. The results demonstrate the application's potential to make online learning more appealing and effective, contributing a novel solution for remote education challenges by integrating gaming elements into the learning process
DESIGN OF FIRE EXTINGUISHER ROBOT USING IOT WITH ANDROID APPLICATION CONTROL
Get PDFFire is an unsupervised incidental disaster. This disaster has a detrimental impact on living and non-living things in the surrounding environment. This study was conducted to design an intelligent firefighting robot using Arduino Mega 2560 and Android-based IoT technology. This firefighting robot uses several Node MCU ESP8266 components as additional devices to connect to wifi. The L298N module regulates the speed and direction of the DC motor rotation, followed by the L9110 fan as hardware to extinguish the fire. The mobile robot prototype uses a DC motor as its driver. In addition, an Android application has been programmed to control the firefighting robot. This application has features that allow the robot to move in various directions and adjust the fan speed when extinguishing fires, all through an internet network connection. The study results showed that the application can be connected within a distance of 1-8 meters with good network quality. The test results showed that at a distance of 1-28 cm, the fan worked very well according to its function, and the Android application also worked optimally. In that range, the fan can extinguish the simulated fire source. The results of this study obtained a new approach to autonomous fire detection and extinguishing using IoT and robotic technology. In addition, it is able to integrate an Android-based IoT controller to enable remote control with real-time monitoring to overcome problems in previous research
PERFORM COMPARATION OF DEEP LEARNING METHODS IN GENDER CLASSIFICATION FROM FACIAL IMAGES
Get PDFIdentifying gender through facial images is a crucial aspect in various life contexts. Biometric technology, such as facial recognition, has become an integral part of various applications, including fraud detection, cybersecurity protection, and consumer behavior analysis. With the advancement of technology and the progress in artificial intelligence, especially through the use of Convolutional Neural Networks (CNNs), computers can now identify gender from facial images with a high level of accuracy. Although there are still some challenges, such as variations in pose, facial expressions, and different lighting conditions, CNNs can overcome these obstacles. This study uses the CelebA dataset, which consists of 122,000 facial images of both men and women. The dataset has been processed to maintain a balanced number of samples for each gender class, resulting in a total of 101,568 samples. The data is divided into training, validation, and test sets, with 80% used for training, and the remaining 20% split between validation and testing. Eight different CNN architectures are applied, including VGG16, VGG19, MobileNetV2, ResNet-50, ResNet-50 V2, Inception V3, Inception ResNet V2, and AlexNet. Although previous research has shown the potential of CNN architectures for various classification tasks, these studies often encounter issues of overfitting on large datasets, which can reduce model accuracy. This study applies dropout techniques and hyperparameter tuning to address overfitting issues and optimize model performance. The training results indicate that ResNet-50, ResNet-50 V2, and Inception V3 achieved the highest accuracy of 98%, while VGG16, VGG19, MobileNetV2, and AlexNet achieved accuracies of 95% and 97%, respectively. Performance evaluation using confusion matrices, precision, recall, and F1-score demonstrates excellent performance
SENTIMENT ANALYSIS OF PLAYER FEEDBACK IN ALGORUN: A STUDY OF DEEP LEARNING MODELS FOR GAME-BASED LEARNING
Get PDFAlgoRun: Coding Game is a game-based learning application aimed at teaching computational thinking (CT) concepts such as variables, conditions, loops, and functions. Evaluating user feedback in such educational games is challenging, as traditional sentiment analysis techniques often overlook nuanced responses. Despite its potential to inform content improvements, sentiment analysis in game-based learning remains underexplored. This study compares the performance of deep learning models—DNN, CNN, RNN with LSTM, and Bidirectional LSTM—for sentiment classification of AlgoRun user reviews, using TF-IDF and word embeddings as feature extraction methods. A total of 1,440 reviews were scraped from the Google Play Store, translated, and preprocessed using data preparation techniques (dropna, fillna), text preprocessing (case folding, cleaning, tokenization, stopword removal, stemming), and feature extraction (TF-IDF and word embeddings). The dataset was labeled into negative, neutral, and positive classes, and split 80% for training and 20% for testing. Among the tested models, the DNN with TF-IDF achieved the highest accuracy of 98.86%, followed by CNN with Word Embeddings (96.97%), Bidirectional LSTM (96.59%), and RNN with LSTM (92.42%). The DNN also showed stable performance and convergence at the 10th epoch, outperforming other models in precision, recall, and F1-score. These results suggest that DNN with TF-IDF is highly effective for sentiment classification in the context of game-based learning. The findings offer useful guidance for developers to adapt content and enhance game quality based on user feedback. This research also contributes to the growing body of literature on leveraging sentiment analysis to optimize educational applications
CLASSIFICATION OF NATURAL DISASTERS IN WEST SEMARANG BASED ON WEATHER DATA USING DEEP LEARNING
Get PDFNatural disasters like floods, landslides, and fires pose serious threats to both life and mental well-being, especially in vulnerable areas like West Semarang, which frequently experiences extreme weather. To mitigate these risks, an accurate classification system is essential for timely prevention and response. This study compares the performance of three neural network models—Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)—in classifying natural disasters using weather data. LSTM and GRU are particularly effective for handling long-term dependencies and addressing vanishing gradient problems common in time series data. Data for the study comes from the Semarang City Regional Disaster Management Agency (BPBD) and the Meteorology, Climatology, and Geophysics Agency (BMKG), spanning 2019 to 2022. The models achieved a high accuracy of 95.8%, but this is due to an imbalanced dataset—70 records of natural disasters versus 1377 without—resulting in classification favoring "no disaster." Among the models, LSTM performed the best, reaching optimal accuracy in just 20.0671 seconds per epoch. This suggests LSTM is the most effective model for this classification task
COMPARATIVE STUDY OF YOLO VERSIONS FOR DETECTING VACANT CAR PARKING SPACES
Get PDFThe increasing vehicle density in urban areas has made parking space availability a significant challenge. With technological advancements, efficient smart parking systems based on object detection have become essential. This study evaluates the performance of YOLO versions 3 to 11 in detecting vacant parking spaces in urban environments, focusing on real-time processing, high accuracy with limited datasets, and adaptability to varying conditions. Using 4,215 annotated images and two test videos, YOLOv7 achieved the highest overall accuracy of 99.57% with an average FPS of 30.79, making it the most effective model for smart parking applications. YOLOv8 and YOLOv11 followed closely, with accuracies of 98.51% and 98.72%, respectively, and average FPS rates of 32.31 and 31.99, balancing precision and speed, which are ideal for real-time applications. Meanwhile, YOLOv5 stood out for its exceptional processing speed of 33.92 FPS. These results highlight YOLO's potential to revolutionize smart parking systems by significantly enhancing both detection precision and operational efficiency.
OPTIMIZING TRANSPORTATION SURVEILLANCE WITH YOLOV7: DETECTION AND CLASSIFICATION OF VEHICLE LICENSE PLATE COLORS
Get PDFOptimizing transportation surveillance requires accurate vehicle license plate color detection and classification; however, existing systems face significant challenges in achieving real-time accuracy and robustness, particularly in crowded traffic scenarios with varying lighting and plate conditions. In Indonesia, vehicle license plates are color-coded based on their usage, including white and black for private vehicles, yellow for public vehicles, red for government vehicles, and green for free-trade areas. Each plate color plays a crucial role in transportation management, enabling proper vehicle identification and regulation. Existing surveillance systems struggle with real-time detection accuracy, especially in distinguishing plate colors in crowded traffic. Traditional methods may not efficiently classify plate colors due to limitations in feature extraction and processing. To address this, this study implements the YOLOv7 model to improve vehicle license plate color detection (black, white, yellow, and red) while distinguishing non-plate vehicles in diverse scenarios. The model's effectiveness is evaluated using precision, recall, and F1-score to ensure robustness for surveillance applications. Results show an average precision of 95.27%, recall of 94.60%, and F1-score of 94.93%, demonstrating strong detection capabilities. Optimizing the Non-Plate category further improves system accuracy, efficiency, and scalability, enhancing transportation monitoring reliability